The Fastest Way To Become A Machine Learning Engineer – Today I’d like to share with you the fastest path to become a Machine Learning Engineer. Now, sit comfortably as I tell you the concrete steps that need to be taken to land a Machine Learning Engineer position. In the beginning, you should be familiar with at least one programming language. It shouldn’t be R though, because Machine Learning Engineers don’t work only on Data Science problems as for example, Data Scientists.
They need to implement whole systems which is more problematic to do it A to Z in R. Python is the most common choice here but Go, Java, C#, or even C++ are all possible options. If you didn’t know any programming language before, Python is the natural choice because it has plenty of libraries starting from data extraction and ending on model deployment. You should start by mastering the language and not diving too deep into Data Science problems.
Websites like Leetcode or HackerRank are your friends. I suggest spending 45-60min every single day on solving algorithmic problems. You will save time in the future due to a lower amount of bugs and faster implementations. For the rest of your time, you should focus on understanding high-level problems. It’s very important to not dive too deep into particular problems in the beginning.
Try to understand as many problems and solutions as possible but don’t dig into them too much. Try to learn what kind of problems you will face and what kind of solutions are out there. At this point, you shouldn’t care too much about concrete implementations. When your general understanding is at a higher level, it means that you know exactly what regression and classification are, what kind of algorithms are out there, and for what kind of problems, particular algorithms work best with.
You can test yourself and try to think about 5 algorithms and their applications in the real world. If you could briefly introduce them to someone with no connection to Data Science, it means you have a high-level understanding of them and you can start digging a little bit deeper. This is the best moment to look for some interesting projects that will help you understand how particular algorithms can be implemented.
At this point, you should learn the most important libraries, which in Python are Pandas, Numpy, Scikit-learn, Keras, and XGBoost. Of course, there are supplements, especially for Scikit-learn, Keras, and XGBoost, but these 3 are the most popular and for starting I don’t see anything better. These 5 libraries for Data Science are like the 5 fingers on your hand – you can do almost anything with them.
Remember, I’m still talking about general understanding – just focus on being able to implement 1 or 2 of the most important use-cases in each of them. Don’t try to learn code by heart. Google has a much better memory – trust me. Being able to copy/paste chunks of code and understand it enough to adjust particular parts to your problem is perfectly enough for now. If you feel comfortable manipulating data using Pandas with Numpy, and you’re familiar with Scikit-learn, Keras, and XGBoost, you can start looking for offers.
Of course, in the beginning, those should be internships or offers for junior Machine Learning Engineers. It’s also a perfect time to start mastering your cloud platform of choice. For me personally, a twist in my career happened when I got to know AWS. I do recommend choosing this platform, as their SageMaker service is astonishing!
You can achieve so many things in such a short time that it was truly unbelievable for me. Clearing Machine Learning Certification was just priceless for my development. A Cloud Guru course along with the Whizlab exam was the cherry on top.
Such certification will let you understand the whole cycle of the Machine Learning pipeline. You will get to know a wide range of algorithms and their pros and cons. At least for AWS certification, you are forced to analyze hundreds (literally hundreds!) of case studies, which lets you get a better understanding of multiple business problems and their solutions.
You will learn that model deployment is not only building an API, but a very complex process highly dependent on a business perspective. Once you get to this point, you’ve probably landed a job already but if not, you’d be perfectly ready to join a Data Science team and build unbelievable projects which is what I hope for you.
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